# Certifiably Safe Manipulation of Deformable Linear Objects via Joint Shape and Tension Prediction

arXiv: https://arxiv.org/abs/2505.13889 · HTML: https://mlsanity.com/paper/2505.13889/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 13 of 13)

- [SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation](https://mlsanity.com/paper/2011.07215/) (2020)
- [Self-Supervised Learning of State Estimation for Manipulating Deformable Linear Objects](https://mlsanity.com/paper/1911.06283/) (2019)
- [Offline-Online Learning of Deformation Model for Cable Manipulation with Graph Neural Networks](https://mlsanity.com/paper/2203.15004/) (2022)
- [EDO-Net: Learning Elastic Properties of Deformable Objects from Graph Dynamics](https://mlsanity.com/paper/2209.08996/) (2022)
- [Differentiable Discrete Elastic Rods for Real-Time Modeling of Deformable Linear Objects](https://mlsanity.com/paper/2406.05931/) (2024)
- [Manipulating Deformable Objects by Interleaving Prediction, Planning, and Control](https://mlsanity.com/paper/2001.09950/) (2020)
- [Can't Touch This: Real-Time, Safe Motion Planning and Control for Manipulators Under Uncertainty](https://mlsanity.com/paper/2301.13308/) (2023)
- [REFINE: Reachability-based Trajectory Design using Robust Feedback Linearization and Zonotopes](https://mlsanity.com/paper/2211.11997/) (2022)
- [Technical Report: Safe, Aggressive Quadrotor Flight via Reachability-based Trajectory Design](https://mlsanity.com/paper/1904.05728/) (2019)
- [Feel the Tension: Manipulation of Deformable Linear Objects in Environments with Fixtures using Force Information](https://mlsanity.com/paper/2310.06424/) (2023)
- [Multi-Modal Data Fusion for Moisture Content Prediction in Apple Drying](https://mlsanity.com/paper/2504.07465/) (2025)
- [Multi-Modal Fusion of In-Situ Video Data and Process Parameters for Online Forecasting of Cookie Drying Readiness](https://mlsanity.com/paper/2504.15599/) (2025)
- [XPG-RL: Reinforcement Learning with Explainable Priority Guidance for Efficiency-Boosted Mechanical Search](https://mlsanity.com/paper/2504.20969/) (2025)

## Built on by (most cited first, 1 of 1)

- [Robustness without Wrinkles: Parallel Simulation and Robust MPC for Certified Deformable Manipulation](https://mlsanity.com/paper/2606.14188/) (2026)

## Abstract (arXiv)

Manipulating deformable linear objects (DLOs) is challenging due to their complex dynamics and the need for safe interaction in contact-rich environments. Most existing models focus on shape prediction alone and fail to account for contact and tension constraints, which can lead to damage to both the DLO and the robot. In this work, we propose a certifiably safe motion planning and control framework for DLO manipulation. At the core of our method is a predictive model that jointly estimates the DLO's future shape and tension. These predictions are integrated into a real-time trajectory optimizer based on polynomial zonotopes, allowing us to enforce safety constraints throughout the execution. We evaluate our framework on a simulated wire harness assembly task using a 7-DOF robotic arm. Compared to state-of-the-art methods, our approach achieves a higher task success rate while avoiding all safety violations. The results demonstrate that our method enables robust and safe DLO manipulation in contact-rich environments.